A global meta-analysis on the effects of organic and inorganic fertilization on grasslands and croplands

A central role for nature-based solution is to identify optimal management practices to address environmental challenges, including carbon sequestration and biodiversity conservation. Inorganic fertilization increases plant aboveground biomass but often causes a tradeoff with plant diversity loss. It remains unclear, however, whether organic fertilization, as a potential nature-based solution, could alter this tradeoff by increasing aboveground biomass without plant diversity loss. Here we compile data from 537 experiments on organic and inorganic fertilization across grasslands and croplands worldwide to evaluate the responses of aboveground biomass, plant diversity, and soil organic carbon (SOC). Both organic and inorganic fertilization increase aboveground biomass by 56% and 42% relative to ambient, respectively. However, only inorganic fertilization decreases plant diversity, while organic fertilization increases plant diversity in grasslands with greater soil water content. Moreover, organic fertilization increases SOC in grasslands by 19% and 15% relative to ambient and inorganic fertilization, respectively. The positive effect of organic fertilization on SOC increases with increasing mean annual temperature in grasslands, a pattern not observed in croplands. Collectively, our findings highlight organic fertilization as a potential nature-based solution that can increase two ecosystem services of grasslands, forage production, and soil carbon storage, without a tradeoff in plant diversity loss.

Egger's regression suggested that there was potential publication bias in aboveground biomass, species richness, Pielou evenness index and soil organic carbon datasets under inorganic fertilization (Supplementary Table 1), while the trim and fill analysis suggested that there were no missing values (Supplementary Figs.2e, f, g and   h).In addition, we found a potential publication bias in aboveground biomass and species richness datasets under organic fertilization, and the trim and fill results are shown in Supplementary Figure 2a-b.The trim and fill analysis suggested that there was no missing value in species richness under organic fertilization (Supplementary Fig. 2b).There were some missing values for aboveground biomass, but we found that the meta-analysis results for aboveground biomass had little impact before and after trimming and filling (before: ln RR++ = 0.37, p < 0.001; after: ln RR++ = 0.19, p < 0.001).Therefore, our meta-analysis results were robust and reliable.

Supplementary Table 2.
Values of weighted mean response ratio (ln RR++) and corresponding confidence intervals in the meta-analysis as shown in Figure 2 in the main text.Source data are provided as a Source Data file.The Note: Inorganic fertilization, Inorg; organic fertilization, Org; ambient conditions, AMB.Value in bold indicates a significant effect (p < 0.05).
To select the set of environmental factors that best predicted the response pattern of biomass, plant diversity and soil organic carbon to nutrient addition, we conducted a multi-model inference procedure using the dredge function in R "MuMIn" package.We conducted a complete predicted model for aboveground biomass, species richness and soil organic carbon with site as a random effect and including all environmental factors as shown in Table 3.The model selected results under inorganic fertilization are shown in Tables S7-8.
Source data are provided as a Source Data file.7. Model preselection results for the effect of aboveground biomass response ratio under inorganic fertilization.See Table 3       Model parameter  2 = 0.756, df = 1, p = 0.385, RMSEA = 0.000, GFI = 0.998, CFI = 1.000Note:  2 : chi-square, p: p value, df: degree of freedom, RMSEA: root mean square error of approximation, GFI: comparative fit index, CFI: goodness of fit index indicating that the models fit reasonably when p > 0.05, RMSEA ≤ 0.08, GFI > 0.90, CFI > 0.95.R 2 represents the proportion of variance explained for the dependent variable.The values in bold indicate a significant effect (two-sided, p < 0.05).See Table 3 for environmental factors.Source data are provided as a Source Data file.Model parameter  2 = 0.007, df = 1, p = 0.935, RMSEA = 0.000, GFI = 1.000,CFI = 1.000Note: When the number of nutrients added (Niche) was included in the model, the fitting coefficient test failed, therefore it was excluded in the final model.Source data are provided as a Source Data file.

Rationale 3
A central role for nature-based solutions is to identify optimal management practices to address environmental challenges, including carbon sequestration and biodiversity conservation.Inorganic fertilization increases plant aboveground biomass but often causes a trade-off with plant diversity loss.It remains unclear, however, whether organic fertilization, as a potential nature-based solution, could alter this tradeoff by increasing aboveground biomass without plant diversity loss Page 2, lines 13-18.

Objectives 4
We explore four main hypotheses: (1) organic fertilization would increase more aboveground biomass than did inorganic fertilization in grasslands, (2) if increased biomass production intensified competition for light, or fertilization reduced belowground niche partitioning, organic fertilization would also cause a decline in plant diversity in grasslands, and (3) if nitrogen detriment (e.g., acidification) was the main mechanism, organic fertilization would not cause plant diversity loss in grasslands.Finally, (4) we hypothesized that organic fertilizer added to croplands would lead to comparable increases in SOC compared to grasslands.

Eligibility criteria 5
We used three criteria to select literature: (1) field experiments were conducted in semi-natural or natural grasslands, or croplands, and included both ambient and nutrient addition treatments; (2) the means, standard errors or standard deviations and sample sizes were reported; and (3) grassland studies reporting exotic plant species introduced by organic fertilization were excluded We used a hierarchical model with inverse variance weighting to summarize the response ratio (ln RR) from all individual studies, as this model was usually appropriate for biological experiments.Because multiple treatments may share a single control, we added "site" as a random factor in the meta-analysis model to account for non-independence of observations collected from the same site.Specifically, we used the "rma.mv"function in R "metafor" package version 4.4.0 to calculate the weighted mean response ratio (ln RR++) and the 95% confidence intervals.The 95% confidence intervals were generated by bootstrapping.When they did not overlap with zero, the treatment effects were considered statistically significant.
Page 19, lines 436-444. 13e We used linear mixed effects models to evaluate the response of biomass, plant diversity and SOC to nutrient fertilization across environmental gradients, with study site as a random effect.We conducted linear mixed models in "lme4" package version 1.1-35.1 and "lmerTest" packages version 3.1.3.To select the set of environmental factors that significantly influenced the response of biomass, plant diversity and SOC to nutrient addition, we conducted a multi-model inference procedure based on the Akaike Information Criterion, using the dredge function in R "MuMIn" package version 1.47.5.To further strengthen the multi-model inference, we also conducted a random forest model to identify the significant environmental predictors of biomass and plant diversity.We used random forest models in the "randomForest" package version 4.7-1.1 to quantify the importance of each predictor, and then used the "rfPermute" package version 2.5.2 to assess the statistical significance of each predictor.
Page 20, lines 465-477. 13f We applied the Egger's test to examine publication bias, and used the trim and fill approach to evaluate the impact of publication bias on the metaanalysis results.
Supplementary Table 1 and Figure 3.

Reporting bias assessment 14
Egger's regression test and funnel plot.Page 19, Supplementary Table 1 and Figure 3.

Certainty assessment 15
We calculate the weighted mean response ratio (ln RR++) and the 95% confidence intervals.The 95% confidence intervals were generated by bootstrapping.When they did not overlap with zero, the treatment effects were considered statistically significant.

Study selection 16a
Studies identified from Web of science and China National Knowledge Network resources: Databases (n=26163) Registers (n=0) Studies removed before screening:1.Duplicate records were removed by using Endnote software (n=2258).2. Records marked as ineligible by automation tools (n=15692).3. Records removed for other reasons, i.e., review papers (n=2118).

Supplementary
Figure 1 Note: Z is the egger regression intercept; p>0.05 indicates that the result is robust without potential publication bias (Egger's test); p<0.05 indicates a potential publication bias.Source data are provided as a Source Data file.Supplementary Figure 3. Funnel plots of potential publication bias of aboveground biomass, species richness, Pielou evenness index and soil organic carbon datasets.Panels a, b, c, and d represent organic fertilization.Panels e, f, g and h represent inorganic fertilization.Source data are provided as a Source Data file.
effects, respectively.The values in bold indicate significant effects (p < 0.05).p value: Probability of type-I error (two-sided).See Table 3 for the environmental factors.Source data are provided as a Source Data file.Supplementary Figure 4.The importance of environmental predictors of aboveground biomass and species richness response ratio under nutrient addition.Organic fertilization (a-b).Inorganic fertilization (c-d).Key: MSE: mean square error estimated by random forest model, MAT: mean annual temperature, TN: soil total nitrogen, Org: organic fertilizer amount added, Sand: soil sand content, pH: soil pH, SWC: soil water content, SOCD: soil organic carbon density, SBD: soil bulk density, SCEC: soil cation exchange capacity.N: nitrogen fertilizer rate, P: phosphorus fertilizer rate, K: potassium fertilizer rate, Niche: the number of nutrients added.Significance level: *p < 0.05 and **p < 0.01.Source data are provided as a Source Data file.
p value: Probability of type-I error (two-sided).Source data are provided as a Source Data file.Supplementary Figure 5. Structural equation model testing the effects of environment factors on aboveground biomass and species richness following nutrient addition.Field experiments that measured both aboveground biomass and species richness were used in these analyses.(a) Organic fertilization.(b) Inorganic fertilization.We used structural equation models (SEM) to explore the direct and indirect effects of biotic and abiotic factors on the response of species richness to nutrient addition.The black solid and gray dashed arrows show significant (two-sided, p < 0.05) and nonsignificant effects (two-sided, p > 0.05), respectively.The bidirectional arrow shows the covariances between variables.The standard path coefficients and significant p-value of the model are shown adjacent to arrows.Nonsignificant coefficients are not shown for simplicity.R 2 represents the proportion of variance explained for the dependent variable (a, b).When the number of nutrients added (Niche) was included in the SEM, the fitting coefficient test failed, therefore it was not included in the final model.Complete model statistical results were presented in Tables 12. Source data are provided as a Source Data file.
table reports two-sided p-values.

Table 8 .
Model preselection results for the effect of species richness response ratio under inorganic fertilization.See Table3for environmental factors.

Table 9 .
Coefficients estimated in the linear mixed model for aboveground biomass, species richness and soil organic carbon response ratio under

:
Marginal R 2 and conditional R 2 indicate R 2 of fixed effects and random plus fixed

Table 10 .
Coefficients estimated in the linear mixed model for aboveground biomass and species richness response ratio under inorganic fertilization.See Table3for the environmental factors and see Table8for other details.

Table 11 .
The coefficients of the linear mixed models for Figure4a.

Table 11 (continued). The
coefficients of the linear mixed models for Figure4b.

Table 12 .
The path coefficients of structural equation models for organic fertilization.Model: Species richness response ratio ~ SWC + SEC + SBD +MAT + TN + N +Org Aboveground biomass response ratio ~ SWC + SCEC + SBD + MAT + TN + N + P

Table 12
We searched for peer-reviewed literature published before 30 October 2022 using the web of science and China National Knowledge Network resources.We used the following keywords: (resource addition OR resource availability OR nutrient addition OR nutrient availability OR nitrogen deposition OR nitrogen addition OR nitrogen enrichment OR phosphorus addition OR phosphorus enrichment OR potassium addition OR potassium enrichment OR organic fertilizer OR organic* OR manure* OR farmyard manure* OR pig manure OR cow manure OR horse manure OR sheep manure OR chicken manure OR wet compost ) AND (species richness OR plant diversity OR biomass OR aboveground biomass OR AGB OR dry matter yield OR SOC OR soil organic carbon OR SOM OR soil organic matter OR SOC storage) AND (grassland OR meadow OR steppe OR prairie OR herbaceous OR annual OR cropland).Studies identified from web of science and China National Knowledge Network resources: Databases (n=26163), Register (n=0).Studies removed before screening: 1. Duplicate records were removed by using Endnote software (n=2258); 2. Records marked as ineligible by automation tools (15692).3. Records removed for other reasons, i.e., review papers.4. Records excluded after abstract and full text screening.Reports excluded: 1.Studies belonged to laboratory cultivation experiments and artificial grasslands (n=2282).2. Studies did not report the means, standard errors or standard deviations (n=1055).3. Studies reported exotic plant species introduced by organic fertilization (n=8).Data were collected simultaneously by Ting-Shuai Shi and Hai-Ling Li from tables in the main text or supporting information when available, or digitally extracted from figures using GetData Graph Digitizer software version 2.26 (http://getdata-graph-digitizer.com/).